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#cuda

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Habr<p>[Перевод] Обзор CUDA: сюрпризы с производительностью</p><p>Наверное, я очень опоздал с изучением CUDA. До недавнего времени даже не знал, что CUDA — это просто C++ с небольшими добавками. Если бы я знал, что изучение её пойдёт как по маслу, я бы столько не медлил. Но, если у вас есть багаж привычек C++ , то код на CUDA у вас будет получаться низкокачественным. Поэтому расскажу вам о некоторых уроках, изученных на практике — возможно, мой опыт поможет вам ускорить код.</p><p><a href="https://habr.com/ru/articles/901750/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">habr.com/ru/articles/901750/</span><span class="invisible"></span></a></p><p><a href="https://zhub.link/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> <a href="https://zhub.link/tags/%D0%BF%D0%B0%D1%80%D0%B0%D0%BB%D0%BB%D0%B5%D0%BB%D0%B8%D0%B7%D0%BC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>параллелизм</span></a> <a href="https://zhub.link/tags/%D0%B3%D1%80%D0%B0%D1%84%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D0%B5_%D0%BF%D1%80%D0%BE%D1%86%D0%B5%D1%81%D1%81%D0%BE%D1%80%D1%8B" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>графические_процессоры</span></a> <a href="https://zhub.link/tags/%D0%BE%D0%BF%D1%82%D0%B8%D0%BC%D0%B8%D0%B7%D0%B0%D1%86%D0%B8%D1%8F" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>оптимизация</span></a></p>
Habr<p>Три икса: новый уровень работы с большими свертками в PyTorch для обучения моделей</p><p>Привет, Хабр! Продолжим разговор про свертки в ML-обучении на C++. Мы уже обсудили, какие есть подходы к реализации сверток, — ссылку на первую часть ищите в конце статьи. Теперь поговорим, как в одном моем проекте нужно было расширить функциональность PyTorch для работы со свертками размерностью больше трех, а потом использовать их в обучении моделей. Сначала рассмотрим, какие ограничения на выбор алгоритма накладывает возможность обучения моделей, а затем изучим два подхода к реализации свертки и адаптируем их к нашей задаче.</p><p><a href="https://habr.com/ru/companies/yadro/articles/899612/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">habr.com/ru/companies/yadro/ar</span><span class="invisible">ticles/899612/</span></a></p><p><a href="https://zhub.link/tags/%D0%BC%D0%B0%D1%88%D0%B8%D0%BD%D0%BD%D0%BE%D0%B5_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>машинное_обучение</span></a> <a href="https://zhub.link/tags/cuda" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>cuda</span></a> <a href="https://zhub.link/tags/convolution" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>convolution</span></a> <a href="https://zhub.link/tags/%D1%81%D0%B2%D0%B5%D1%80%D1%82%D0%BE%D1%87%D0%BD%D1%8B%D0%B5_%D0%BD%D0%B5%D0%B9%D1%80%D0%BE%D0%BD%D0%BD%D1%8B%D0%B5_%D1%81%D0%B5%D1%82%D0%B8" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>сверточные_нейронные_сети</span></a></p>
Maquinari.cat<p>Els xinesos de Moore Threads volen portejar els software CUDA de Nvidia a les seves GPU sota el seu stack anomenat MUSA.</p><p><a href="https://www.tomshardware.com/pc-components/gpus/chinas-moore-threads-polishes-homegrown-cuda-alternative-musa-supports-porting-cuda-code-using-musify-toolkit" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">tomshardware.com/pc-components</span><span class="invisible">/gpus/chinas-moore-threads-polishes-homegrown-cuda-alternative-musa-supports-porting-cuda-code-using-musify-toolkit</span></a></p><p><a href="https://mastodon.social/tags/Nvidia" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Nvidia</span></a> <a href="https://mastodon.social/tags/MooreThreads" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MooreThreads</span></a> <a href="https://mastodon.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> <a href="https://mastodon.social/tags/MUSA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MUSA</span></a></p>
HGPU group<p>Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs</p><p><a href="https://mast.hpc.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> <a href="https://mast.hpc.social/tags/PTX" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PTX</span></a> <a href="https://mast.hpc.social/tags/HPC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HPC</span></a> <a href="https://mast.hpc.social/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LLM</span></a> <a href="https://mast.hpc.social/tags/PyTorch" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyTorch</span></a> <a href="https://mast.hpc.social/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DeepLearning</span></a> <a href="https://mast.hpc.social/tags/DL" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DL</span></a></p><p><a href="https://hgpu.org/?p=29863" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">hgpu.org/?p=29863</span><span class="invisible"></span></a></p>
♡ Eva Winterschön ♡<p>💻 FreeBSD CUDA drm-61-kmod 💻</p><p>"Just going to test the current pkg driver, this will only take a second...", the old refrain goes. Surely, it will not punt away an hour or so of messing about in loader.conf on this EPYC system... </p><p>- Here are some notes to back-track a botched/crashing driver kernel panic situation. <br>- Standard stuff, nothing new over the years here with loader prompt. <br>- A few directives are specific to this system, though may provide a useful general reference. <br>- The server has an integrated GPU in addition to nvidia pcie, so a module blacklist for the "amdgpu" driver is necessary (EPYC 4564P).</p><p>Step 1: during boot-up, "exit to loader prompt"<br>Step 2: set/unset the values as needed at the loader prompt</p><p>unset nvidia_load<br>unset nvidia_modeset_load<br>unset hw.nvidiadrm.modeset<br>set module_blacklist=amdgpu,nvidia,nvidia_modeset<br>set machdep.hyperthreading_intr_allowed=0<br>set verbose_loading=YES<br>set boot_verbose=YES<br>set acpi_dsdt_load=YES<br>set audit_event_load=YES<br>kern.consmsgbuf_size=1048576<br>set loader_menu_title=waffenschwester<br>boot</p><p>Step 3: login to standard tty shell <br>Step 4: edit /boot/loader.conf (and maybe .local)<br>Step 5: edit /etc/rc.conf (and maybe .local)<br>Step 6: debug the vast output from kern.consmsgbuf logs</p><p><a href="https://mastodon.bsd.cafe/tags/freebsd" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>freebsd</span></a> <a href="https://mastodon.bsd.cafe/tags/nvidia" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>nvidia</span></a> <a href="https://mastodon.bsd.cafe/tags/cuda" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>cuda</span></a> <a href="https://mastodon.bsd.cafe/tags/gpu" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>gpu</span></a> <a href="https://mastodon.bsd.cafe/tags/engineering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>engineering</span></a> <a href="https://mastodon.bsd.cafe/tags/terminal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>terminal</span></a> <a href="https://mastodon.bsd.cafe/tags/saturday" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>saturday</span></a></p>
GripNews<p>🌕 GitHub - Rust-GPU/Rust-CUDA:使用 Rust 撰寫和執行快速 GPU 程式碼的生態系統<br>➤ 打造 Rust 在 GPU 計算領域的地位<br>✤ <a href="https://github.com/Rust-GPU/Rust-CUDA" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">github.com/Rust-GPU/Rust-CUDA</span><span class="invisible"></span></a><br>Rust-CUDA 是一個專案,旨在使 Rust 成為使用 CUDA 工具包進行高效能 GPU 計算的首選語言。它提供了一系列函式庫和工具,可將 Rust 編譯為快速的 PTX 程式碼,並與現有的 CUDA 函式庫整合。 該專案包含 `rustc_codegen_nvvm` (Rust 編譯器後端)、`cuda_std` (GPU 端功能)、`cudnn` (深度神經網路加速)、`cust` (CPU 端 CUDA 功能)、`gpu_rand` (GPU 隨機數產生) 和 `optix` (光線追蹤) 等多個 crates,旨在覆蓋整個 CUDA 生態系統。 儘管目前仍處於早期開發階段,但 Rust-CUDA 旨在克服以往 Rust 與 CUDA 整合的困難,並充分利用 Rust 的優勢,如效能<br><a href="https://mastodon.social/tags/%E9%96%8B%E7%99%BC%E5%B7%A5%E5%85%B7" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>開發工具</span></a> <a href="https://mastodon.social/tags/GPU" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GPU</span></a> <a href="https://mastodon.social/tags/Rust" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Rust</span></a> <a href="https://mastodon.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a></p>
Hacker News 50<p>Rust CUDA Project</p><p>Link: <a href="https://github.com/Rust-GPU/Rust-CUDA" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">github.com/Rust-GPU/Rust-CUDA</span><span class="invisible"></span></a><br>Discussion: <a href="https://news.ycombinator.com/item?id=43654881" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">news.ycombinator.com/item?id=4</span><span class="invisible">3654881</span></a></p><p><a href="https://social.lansky.name/tags/rust" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>rust</span></a> <a href="https://social.lansky.name/tags/cuda" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>cuda</span></a></p>
Denzil Ferreira :fedora:<p>Been fighting the whole day trying to get ROCm to play nice with 780M and PyTorch. Using latest <a href="https://techhub.social/tags/rocm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>rocm</span></a> and my laptop just freezes with gfx1103 and using HSA override to 11.0.0 and with 10.3.0 :blobcatknife: </p><p><a href="https://techhub.social/tags/amd" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>amd</span></a> really needs to fix this crap for their GPUs. Using Docker and their provided ROCm images. I know, 780M is not supported. But c’mon, ALL Nvidia cards can run <a href="https://techhub.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> just fine. <a href="https://techhub.social/tags/rant" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>rant</span></a></p>
Hacker News<p>Rust CUDA Project</p><p><a href="https://github.com/Rust-GPU/Rust-CUDA" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">github.com/Rust-GPU/Rust-CUDA</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HackerNews</span></a> <a href="https://mastodon.social/tags/Rust" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Rust</span></a> <a href="https://mastodon.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> <a href="https://mastodon.social/tags/Project" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Project</span></a> <a href="https://mastodon.social/tags/Rust" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Rust</span></a> <a href="https://mastodon.social/tags/GPU" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GPU</span></a> <a href="https://mastodon.social/tags/Programming" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Programming</span></a> <a href="https://mastodon.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> <a href="https://mastodon.social/tags/Development" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Development</span></a> <a href="https://mastodon.social/tags/Tech" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Tech</span></a> <a href="https://mastodon.social/tags/Innovation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Innovation</span></a></p>
Habr<p>Ведущий разработчик ChatGPT и его новый проект — Безопасный Сверхинтеллект</p><p>Многие знают об Илье Суцкевере только то, что он выдающийся учёный и программист, родился в СССР, соосновал OpenAI и входит в число тех, кто в 2023 году изгнал из компании менеджера Сэма Альтмана. А когда того вернули, Суцкевер уволился по собственному желанию в новый стартап Safe Superintelligence («Безопасный Сверхинтеллект»). Илья Суцкевер действительно организовал OpenAI вместе с Маском, Брокманом, Альтманом и другими единомышленниками, причём был главным техническим гением в компании. Ведущий учёный OpenAI сыграл ключевую роль в разработке ChatGPT и других продуктов. Сейчас Илье всего 38 лет — совсем немного для звезды мировой величины.</p><p><a href="https://habr.com/ru/companies/ruvds/articles/892646/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">habr.com/ru/companies/ruvds/ar</span><span class="invisible">ticles/892646/</span></a></p><p><a href="https://zhub.link/tags/%D0%98%D0%BB%D1%8C%D1%8F_%D0%A1%D1%83%D1%86%D0%BA%D0%B5%D0%B2%D0%B5%D1%80" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Илья_Суцкевер</span></a> <a href="https://zhub.link/tags/Ilya_Sutskever" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Ilya_Sutskever</span></a> <a href="https://zhub.link/tags/OpenAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>OpenAI</span></a> <a href="https://zhub.link/tags/10x_engineer" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>10x_engineer</span></a> <a href="https://zhub.link/tags/AlexNet" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AlexNet</span></a> <a href="https://zhub.link/tags/Safe_Superintelligence" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Safe_Superintelligence</span></a> <a href="https://zhub.link/tags/ImageNet" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ImageNet</span></a> <a href="https://zhub.link/tags/%D0%BD%D0%B5%D0%BE%D0%BA%D0%BE%D0%B3%D0%BD%D0%B8%D1%82%D1%80%D0%BE%D0%BD" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>неокогнитрон</span></a> <a href="https://zhub.link/tags/GPU" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GPU</span></a> <a href="https://zhub.link/tags/GPGPU" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GPGPU</span></a> <a href="https://zhub.link/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> <a href="https://zhub.link/tags/%D0%BA%D0%BE%D0%BC%D0%BF%D1%8C%D1%8E%D1%82%D0%B5%D1%80%D0%BD%D0%BE%D0%B5_%D0%B7%D1%80%D0%B5%D0%BD%D0%B8%D0%B5" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>компьютерное_зрение</span></a> <a href="https://zhub.link/tags/LeNet" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LeNet</span></a> <a href="https://zhub.link/tags/Nvidia_GTX" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Nvidia_GTX</span></a>&nbsp;580 <a href="https://zhub.link/tags/DNNResearch" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DNNResearch</span></a> <a href="https://zhub.link/tags/Google_Brain" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Google_Brain</span></a> <a href="https://zhub.link/tags/%D0%90%D0%BB%D0%B5%D0%BA%D1%81_%D0%9A%D1%80%D0%B8%D0%B6%D0%B5%D0%B2%D1%81%D0%BA%D0%B8" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Алекс_Крижевски</span></a> <a href="https://zhub.link/tags/%D0%94%D0%B6%D0%B5%D1%84%D1%84%D1%80%D0%B8_%D0%A5%D0%B8%D0%BD%D1%82%D0%BE%D0%BD" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Джеффри_Хинтон</span></a> <a href="https://zhub.link/tags/Seq2seq" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Seq2seq</span></a> <a href="https://zhub.link/tags/TensorFlow" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>TensorFlow</span></a> <a href="https://zhub.link/tags/AlphaGo" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AlphaGo</span></a> <a href="https://zhub.link/tags/%D0%A2%D0%BE%D0%BC%D0%B0%D1%88_%D0%9C%D0%B8%D0%BA%D0%BE%D0%BB%D0%BE%D0%B2" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Томаш_Миколов</span></a> <a href="https://zhub.link/tags/Word2vec" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Word2vec</span></a> <a href="https://zhub.link/tags/fewshot_learning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>fewshot_learning</span></a> <a href="https://zhub.link/tags/%D0%BC%D0%B0%D1%88%D0%B8%D0%BD%D0%B0_%D0%91%D0%BE%D0%BB%D1%8C%D1%86%D0%BC%D0%B0%D0%BD%D0%B0" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>машина_Больцмана</span></a> <a href="https://zhub.link/tags/%D1%81%D0%B2%D0%B5%D1%80%D1%85%D0%B8%D0%BD%D1%82%D0%B5%D0%BB%D0%BB%D0%B5%D0%BA%D1%82" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>сверхинтеллект</span></a> <a href="https://zhub.link/tags/GPT" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GPT</span></a> <a href="https://zhub.link/tags/ChatGPT" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ChatGPT</span></a> <a href="https://zhub.link/tags/ruvds_%D1%81%D1%82%D0%B0%D1%82%D1%8C%D0%B8" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ruvds_статьи</span></a></p>
Hacker News 50<p>Nvidia adds native Python support to CUDA</p><p>Link: <a href="https://thenewstack.io/nvidia-finally-adds-native-python-support-to-cuda/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">thenewstack.io/nvidia-finally-</span><span class="invisible">adds-native-python-support-to-cuda/</span></a><br>Discussion: <a href="https://news.ycombinator.com/item?id=43581584" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">news.ycombinator.com/item?id=4</span><span class="invisible">3581584</span></a></p><p><a href="https://social.lansky.name/tags/cuda" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>cuda</span></a> <a href="https://social.lansky.name/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://social.lansky.name/tags/nvidia" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>nvidia</span></a></p>
N-gated Hacker News<p>NVIDIA finally joins the 21st century by adding <a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> support to <a href="https://mastodon.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a>, because who needs cutting-edge tech when you can just catch up with 2006? 🕰️ Meanwhile, The New Stack is begging you to re-subscribe like a clingy ex who just can't take a hint. 📧💔<br><a href="https://thenewstack.io/nvidia-finally-adds-native-python-support-to-cuda/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">thenewstack.io/nvidia-finally-</span><span class="invisible">adds-native-python-support-to-cuda/</span></a> <a href="https://mastodon.social/tags/NVIDIA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NVIDIA</span></a> <a href="https://mastodon.social/tags/TheNewStack" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>TheNewStack</span></a> <a href="https://mastodon.social/tags/TechNews" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>TechNews</span></a> <a href="https://mastodon.social/tags/Subscribe" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Subscribe</span></a> <a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HackerNews</span></a> <a href="https://mastodon.social/tags/ngated" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ngated</span></a></p>
Hacker News<p>Nvidia adds native Python support to CUDA</p><p><a href="https://thenewstack.io/nvidia-finally-adds-native-python-support-to-cuda/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">thenewstack.io/nvidia-finally-</span><span class="invisible">adds-native-python-support-to-cuda/</span></a></p><p><a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HackerNews</span></a> <a href="https://mastodon.social/tags/Nvidia" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Nvidia</span></a> <a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://mastodon.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> <a href="https://mastodon.social/tags/TechNews" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>TechNews</span></a></p>
Amartya<p>My brain is absolutely fried. <br>Today is the last day of coursework submissions for this semester. What a hectic month. <br>DNN with PyTorch, Brain model parallelisation with MPI, SYCL and OpenMP offloading of percolation models,hand optimizing serial codes for performance.<br>Two submissions due today. Submitted one and finalising my report for the second one. <br>Definitely having a pint after this</p><p><a href="https://fosstodon.org/tags/sycl" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sycl</span></a> <a href="https://fosstodon.org/tags/hpc" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>hpc</span></a> <a href="https://fosstodon.org/tags/msc" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>msc</span></a> <a href="https://fosstodon.org/tags/epcc" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>epcc</span></a> <a href="https://fosstodon.org/tags/cuda" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>cuda</span></a> <a href="https://fosstodon.org/tags/pytorch" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pytorch</span></a> <a href="https://fosstodon.org/tags/mpi" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>mpi</span></a> <a href="https://fosstodon.org/tags/openmp" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>openmp</span></a> <a href="https://fosstodon.org/tags/hectic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>hectic</span></a> <a href="https://fosstodon.org/tags/programming" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>programming</span></a> <a href="https://fosstodon.org/tags/parallelprogramming" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>parallelprogramming</span></a> <a href="https://fosstodon.org/tags/latex" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>latex</span></a></p>
Habr<p>[Перевод] «Я ненавижу C++, но восхищаюсь его мастерами»: Дженсен Хуанг (Nvidia) о том, как ИИ вернулся домой</p><p>Nvidia давно вышла за пределы игровых миров — сегодня её технологии формируют будущее ИИ, научных исследований, связи и многого другого. Но как компания, начавшая с графики, стала флагманом искусственного интеллекта? В интервью для Computerphile (25.03.2025) Хуанг рассказывает, как закон Амдала уживается с тензорными ядрами, а CUDA из инструмента разработчика превратилась в основу для преобразования индустрий. Это интервью о процессе, в котором технологии развиваются, пересекаются и возвращаются туда, с чего начинали.</p><p><a href="https://habr.com/ru/companies/bothub/articles/895682/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">habr.com/ru/companies/bothub/a</span><span class="invisible">rticles/895682/</span></a></p><p><a href="https://zhub.link/tags/ai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ai</span></a> <a href="https://zhub.link/tags/%D0%B8%D0%B8" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ии</span></a> <a href="https://zhub.link/tags/%D0%B4%D0%B6%D0%B5%D0%BD%D1%81%D0%B5%D0%BD_%D1%85%D1%83%D0%B0%D0%BD%D0%B3" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>дженсен_хуанг</span></a> <a href="https://zhub.link/tags/nvidia" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>nvidia</span></a> <a href="https://zhub.link/tags/cuda" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>cuda</span></a> <a href="https://zhub.link/tags/transformer" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>transformer</span></a> <a href="https://zhub.link/tags/%D0%B7%D0%B0%D0%BA%D0%BE%D0%BD_%D0%B0%D0%BC%D0%B4%D0%B0%D0%BB%D0%B0" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>закон_амдала</span></a> <a href="https://zhub.link/tags/5g" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>5g</span></a></p>
Hacker News 50<p>Ask HN: Why hasn't AMD made a viable CUDA alternative?</p><p>Discussion: <a href="https://news.ycombinator.com/item?id=43547309" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">news.ycombinator.com/item?id=4</span><span class="invisible">3547309</span></a></p><p><a href="https://social.lansky.name/tags/cuda" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>cuda</span></a></p>
pafurijaz<p>It seems that <a href="https://mastodon.social/tags/Vulkan" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Vulkan</span></a> could be the real alternative for using <a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a> on GPUs or CPUs of any brand, without necessarily having to rely on <a href="https://mastodon.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> or <a href="https://mastodon.social/tags/AMD" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AMD</span></a>'s <a href="https://mastodon.social/tags/ROCm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ROCm</span></a>. I thought <a href="https://mastodon.social/tags/SYCL" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SYCL</span></a> was the alternative. This might finally free us from of monopoly <a href="https://mastodon.social/tags/Nvidia" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Nvidia</span></a>.<br><a href="https://mastodon.social/tags/Khronos" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Khronos</span></a></p>
HGPU group<p>Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle</p><p><a href="https://mast.hpc.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> <a href="https://mast.hpc.social/tags/HIP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HIP</span></a> <a href="https://mast.hpc.social/tags/OpenMP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>OpenMP</span></a> <a href="https://mast.hpc.social/tags/HPC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HPC</span></a> <a href="https://mast.hpc.social/tags/Package" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Package</span></a></p><p><a href="https://hgpu.org/?p=29849" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">hgpu.org/?p=29849</span><span class="invisible"></span></a></p>
HGPU group<p>TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives</p><p><a href="https://mast.hpc.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> <a href="https://mast.hpc.social/tags/PTX" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PTX</span></a> <a href="https://mast.hpc.social/tags/Triton" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Triton</span></a> <a href="https://mast.hpc.social/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LLM</span></a> <a href="https://mast.hpc.social/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DeepLearning</span></a> <a href="https://mast.hpc.social/tags/DL" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DL</span></a></p><p><a href="https://hgpu.org/?p=29841" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">hgpu.org/?p=29841</span><span class="invisible"></span></a></p>
HGPU group<p>Efficient allocation of image recognition and LLM tasks on multi-GPU system</p><p><a href="https://mast.hpc.social/tags/CUDA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CUDA</span></a> <a href="https://mast.hpc.social/tags/Performance" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Performance</span></a> <a href="https://mast.hpc.social/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LLM</span></a> <a href="https://mast.hpc.social/tags/ImageRecognition" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ImageRecognition</span></a></p><p><a href="https://hgpu.org/?p=29839" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">hgpu.org/?p=29839</span><span class="invisible"></span></a></p>